Evaluation of Neural Networks Performance in Active Cancellation of Acoustic Noise

Majlesi Journal of Electrical Engineering

مقاله 1، دوره 8، شماره 4، بهمن 2014، صفحه 1-7   

نوع مقاله: Review Article

نویسندگان

Mehrshad Salmasi^{* 1}؛ Homayoun Mahdavi-Nasab²

¹Young Researchers and Elite Club, Najafabad Branch, Islamic Azad University, Najafabad, Isfahan, Iran

²Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Isfahan, Iran

چکیده

Active noise control (ANC) works on the principle of destructive interference between the primary disturbance field heard as undesired noise and secondary field which is generated from control actuators. In the simplest system, the disturbance field can be a simple sine wave, and the secondary field is the same sine wave but 180 degrees out of phase. This research presents an investigation on the use of different types of neural networks in active noise control. Performance of the multilayer perceptron (MLP), Elman and generalized regression neural networks (GRNN) in active cancellation of acoustic noise signals is investigated and compared in this paper. Acoustic noise signals are selected from a SPIB database. In order to compare the networks appropriately, similar structures and similar training and test samples are deduced for neural networks. The simulation results show that MLP, GRNN, and Elman neural networks present proper performance in active cancellation of acoustic noise. It is concluded that Elman and MLP neural networks have better performance than GRNN in noise attenuation. It is demonstrated that designed ANC system achieve good noise reduction in low frequencies. 

کلیدواژه‌ها

Generalized Regression Neural Network (GRNN)؛ en؛ Elman Neural Network؛ MLP Neural network؛ Active Noise Control (ANC)؛ Feedback Active Noise Control System (FANC)؛ SPIB Database

مراجع

[1] S.M., Kuo, D.R., Morgan, “Active noise control: a tutorial review,” Proc. IEEE, Vol. 87, No.6, pp.943-973, June 1999.

[2] N.V. George, G., Panda, “Advances in active noise control: A survey, with emphasis on recent nonlinear techniques,” Signal Processing Journal, Vol. 93, pp. 363-377, 2013.

[3] S.M., Kuo , D.R., Morgan, “Active noise control systems, algorithms and DSP implementations,” New York: Wiley, 1996.

[4] C., Hansen, S., Snyder, X., Qiu, L., Brooks and D., Moreau, “Active control of noise and vibration,” Second Edition, CRC Press, 2012.

[5] Y.L., Zhou, Q.Z., Zhang, X.D., Li, and W.S., Gan, “Analysis and DSP implementation of an ANC system using a filtered–error neural network,” Journal of Sound and Vibration, Vol. 285, Issue 1-2, pp. 1-25, Aug. 2004.

[6] Y., Song, Y., Gong, and S.M., Kuo, “A robust hybrid feedback active noise cancellation headset,” IEEE Trans. on Speech and Audio Processing, Vol.13, Issue.4 , pp. 607-617, July 2005.

[7] T., Meurers, S., M. Veres, M., Sandor, and S.J., Elliot, “Frequency selective feedback for active noise control,” IEEE Control Systems, Vol. 22, Issue 4 , pp. 32-41, Aug. 2002.

[8] C.H., Hansen, “Current and future industrial applications of active noise control,” Noise Control Engineering Journal, Vol. 53, pp. 181-196, 2005.

[9] D., Oliveira, P.R., Leopoldo, K., Janssens, P., Gajdatsy, H.V., Auweraer, P.S., Varoto, O., Sas, and W., Desmet, “Active sound quality control of engine induced cavity noise,” Mechanical Systems and Signal Processing Journal, Vol.23, No.2, pp. 476-488, Feb. 2009.

[10] Y.L., Zhou, Q.Z., Zhang, X.D., Li, and W.S., Gan, “On the use of an SPSA-based model-free feedback controller in active noise control for periodic disturbances in a duct,” Journal of Sound and Vibration, Vol.317, pp. 456-472, 2008.

[11] W.S., Gan, S., Mitra, and S.M., Kuo, “Adaptive feedback active noise control headset: Implementation, evaluation and its extensions,” IEEE Transactions on Consumer Electronics, Vol. 51, No. 3, pp.975-982, Aug. 2005.

[12] C.K., Chen, T.D., Chiueh and J.H., Chen “Active cancellation system of acoustic noise in MR Imaging,” IEEE Trans. on Biomedical Engineering, Vol. 46, No.2, pp.186-191, Feb. 1999.

[13] S., Haykin, “Neural networks-A comprehensive foundation,” Second Edition, Prentice-Hall Incorporation, New Jersey, USA, 1999.

[14] P.D., Wasserman, “Advanced methods in neural computing,” New York: Van Nostrand Reinhold, 1993.

[15] J.L., Elman, “Finding structure in time,” Cognitive Science, Vol.14, pp. 179-211, 1990.

[16] Rice University, Signal Processing Information Base [Online], http://spib.rice.edu/spib/select_noise.html.

[17] A.A.. Milani, G.. Kannan, and I.M.S., Panahi, “On maximum achievable noise reduction in ANC systems,” In Proc. IEEE Int. Conf. Acoustics speech & Signal Processing, pp. 349-352, Dallas, March 2010.

[18] G., Kannan, A.A., Milani, G., Kannan, I.M.S., Panahi, and N., Kehtarnavaz, “Performance enhancement of adaptive active noise control systems for FMRI machines,” In Proc. IEEE Int. Conf. Engineering in Medicine and Biology Society (EMBS), pp. 4327-4330, Buenos Aires, Argentina, September 2010.

[19] C.K., Chen and T.D., Chiueh, “Multilayer perceptron neural networks for active noise cancellation,” In Proc. IEEE Int. Symp. Circits and Systems, Vol. 3, pp. 523-526, May 1996.